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Peptidome profiling dataset of ovarian cancer and non-cancer proximal fluids: Ascites and blood sera

Despite a large number of proteomic studies of biological fluids from ovarian cancer patients, there is a lack of sensitive screening methods in clinical practice (Kim et al., 2016) (DOI:https://doi.org/10.1111/cas.12987[1]). Low molecular weight endogenous peptides more easily diffuse across endoth...

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Detalles Bibliográficos
Autores principales: Shender, Victoria, Arapidi, Georgij, Butenko, Ivan, Anikanov, Nikolay, Ivanova, Olga, Govorun, Vadim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321966/
https://www.ncbi.nlm.nih.gov/pubmed/30627607
http://dx.doi.org/10.1016/j.dib.2018.12.056
Descripción
Sumario:Despite a large number of proteomic studies of biological fluids from ovarian cancer patients, there is a lack of sensitive screening methods in clinical practice (Kim et al., 2016) (DOI:https://doi.org/10.1111/cas.12987[1]). Low molecular weight endogenous peptides more easily diffuse across endothelial barriers than proteins and can be more relevant biomarker candidates (Meo et al., 2016) (DOI:https://doi.org/10.18632/oncotarget.8931[2], (Bery et al., 2014) DOI:https://doi.org/10.1186/1559-0275-11-13[3], (Huang et al., 2018) DOI:https://doi.org/10.1097/IGC.0000000000001166[4]). Detailed peptidomic analysis of 26 ovarian cancer and 15 non-cancer samples of biological fluids (ascites and sera) were performed using TripleTOF 5600+ mass-spectrometer. Prior to LC-MS/MS analysis, peptides were extracted from biological fluids using anion exchange sorbent with subsequent peptide desorption from the surface of highly abundant proteins. In total, we identified 4874 peptides; 3123 peptides were specific for the ovarian cancer samples. The mass-spectrometry peptidomics data presented in this data article have been deposited to the ProteomeXchange Consortium (Deutsch et al., 2017) (DOI:https://doi.org/10.1093/nar/gkw936[5]) via the PRIDE partner repository with the dataset identifier PXD009382 and https://doi.org/10.6019/PXD009382, http://www.ebi.ac.uk/pride/archive/projects/PXD009382.